Document compression with neighborhood biased pixel labeling

ABSTRACT

To compress a document, a number of edges present in a selected portion of the document are counted to determine whether the number of edges exceeds a threshold. When the number of edges exceeds the threshold, a pixel is selected from the portion and a set of neighboring pixels is identified for the pixel. For each neighboring pixel in a subset of the neighboring pixels, a corresponding label of the neighboring pixel is identified. A mask layer contains labels of pixels in the portion where a label of the selected pixel is biased using labels of neighboring pixels in the subset of the neighboring pixels. The selected pixel is designated to a foreground or a background layer of the document according to the label of the selected pixel. A compressed document is constructed corresponding to the document using the mask layer, the foreground layer, and the background layer.

TECHNICAL FIELD

The present invention relates generally to a method, system, andcomputer program product for document compression. More particularly,the present invention relates to a method, system, and computer programproduct for document compression with neighborhood biased pixellabeling.

BACKGROUND

Document or image compression is collectively referred to herein as“document compression” or simply, “compression”. Document compressionaddresses the problem of reducing the amount of data required torepresent a digital content of a given document or image. The underlyingprinciple of the reduction process in document compression is theremoval of redundant data.

Compression techniques generally fall into two broad categories—losslessand lossy. A lossless compression preserves the information in that itallows the data of the document or image to be compressed anddecompressed without the loss of information. While the informationreproductions from a lossless compression results in the originalinformation, in many circumstances, lossless compression provides littleor no reduction in the data size. On the other hand, lossy compressionoften provides comparatively higher levels of data reduction but resultin a less than perfect reproduction of the original information.

For example, lossless compression methods such as Lempel-Ziv (LZ) do notperform particularly well on scanned images and achieve little to nosize reduction from the compression. While lossy compression methods,such as Joint Photographic Experts Group (JPEG) compression, work fairlywell on continuous-tone pixel maps in reducing their size, they do notwork particularly well on the parts of the page containing text, as theclarity of the text is lost in the reproduction due to data loss duringcompression.

SUMMARY

The illustrative embodiments provide a method, system, and computerprogram product for document compression with neighborhood biased pixellabeling. An embodiment includes a method for compressing a document.The embodiment counts a number of edges present in a selected portion ofthe document. The embodiment determines whether the number of edgesexceeds a threshold number of edges. The embodiment selects, responsiveto the number of edges exceeding the threshold number of edges, a pixelfrom the portion. The embodiment identifies, for the pixel, a set ofneighboring pixels. The embodiment identifies, for each neighboringpixel in a subset of the set of neighboring pixels, a correspondinglabel of the neighboring pixel. A mask layer corresponding to thedocument contains labels of pixels in the portion. The embodimentbiases, in the mask layer, a label of the selected pixel using labels ofneighboring pixels in the subset of the neighboring pixels. Theembodiment designates, according to the label of the selected pixel, theselected pixel to one of a foreground layer corresponding to thedocument and a background layer corresponding to the document. Theembodiment constructs, corresponding to the document, a compresseddocument using the mask layer, the foreground layer, and the backgroundlayer.

Another embodiment includes a computer program product for compressing adocument, the computer program product comprising one or morecomputer-readable storage devices, and program instructions stored on atleast one of the one or more storage devices.

Another embodiment includes a computer system for compressing adocument, the computer system comprising one or more processors, one ormore computer-readable memories, and one or more computer-readablestorage devices, and program instructions stored on at least one of theone or more storage devices for execution by at least one of the one ormore processors via at least one of the one or more memories.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofthe illustrative embodiments when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented;

FIG. 2 depicts a block diagram of a data processing system in whichillustrative embodiments may be implemented;

FIG. 3 depicts a block diagram of an example document compression methodin accordance with an illustrative embodiment;

FIG. 4 depicts a block diagram of an example process for documentcompression with neighborhood biased pixel labeling in accordance withan illustrative embodiment;

FIG. 5 depicts a block diagram of an example pixel neighborhood for PHLin accordance with an illustrative embodiment;

FIG. 6 depicts a block diagram of a compression of layers resulting froma modified FCM process in accordance with an illustrative embodiment;and

FIG. 7 depicts a flowchart of an example process for documentcompression with neighborhood biased pixel labeling in accordance withan illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize that one approach to satisfy thecompression needs of differing types of data has been to use the MixedRaster Content (MRC) format to describe the original information. In anMRC representation, the information, e.g., a composite image having textintermingled with color or gray scale information is segmented into twoor more planes or layers, generally referred to as the upper and lowerplanes or layers. A selector plane or layer is generated to indicate,for each pixel, which of the image layers contains the actual image datathat should be used to reconstruct the final output image.

Separating the layers in this manner can improve the compression of theimage because the data can be arranged such that the layers are smootherand more compressible than the original image. Separation of layers alsoallows different compression methods to be applied to the differentlayers. Thus, a compression technique that is appropriate for the typeof data residing on a particular layer can be applied to that layer.

For example, assume that a color or gray scale pixel map represents adocument. An MRC process decomposes the pixel map into a three-layerrepresentation—a reduced-resolution foreground layer, areduced-resolution background layer, and a high-resolution binaryselector layer. The reduced-resolution is a resolution, e.g., pixels perinch, that is lower than a resolution of the original data. Theforeground layer contains the color or gray scale information offoreground items such as text. The background layer contains the coloror gray scale information for the background of the page and thecontinuous tone pictures that are contained on the page. The selectorlayer stores information for selecting from either the foreground layeror background layer during decompression.

The illustrative embodiments recognize that the MRC method classifiespixels according to their frequency in the original data. Generally, theMRC method classifies high frequency pixels, to wit, pixels appearing ator above a threshold number of times, as text pixels and places them onthe foreground layer. Low frequency pixels, to wit, pixels appearing ata lower than a threshold number of times, as image pixels and placesthem on the background layer.

Accordingly, the illustrative embodiments recognize that the MRC methodoften misclassifies pixels belonging to halftone dots larger than acertain size in the original data and noise—such as the salt and pepperdots on some document images, as high frequency pixels and classifiesthem as text. This misclassification increases the size of thecompressed data in the MRC method.

The inventors are unaware of any general recognition of these problemsby fellow practitioners. The illustrative embodiments used to describethe invention generally address and solve the above-described problemsand other problems related to document compression. The illustrativeembodiments provide a method, system, and computer program product fordocument compression with neighborhood biased pixel labeling.

An embodiment decomposes an original document into three planes orlayers—an upper plane or upper layer, a lower plane or lower layer, anda selector plane or selector layer. The selector plane or layer is alsoknown as a selection plane or selection layer, or a mask plane or masklayer. The embodiment includes text areas in a high-resolution selectorlayer. The embodiment creates the upper layer as a low-resolutionforeground, representing the color of the text. The embodiment createsthe lower layer as a low-resolution background containing the remainingpixels.

The low-resolution or reduced resolution is any resolution at or belowthe resolution of the original document, at which a reader or perceiverof the document cannot distinguish between resolution values. Forexample, to a human eye, a document appears to be indistinguishable at600 pixels per inch and 300 pixels per inch. Similarly, to a scannertool or application, the text is just as readable whether the resolutionis 300 pixels per inch or 60 pixels per inch. Reducing the resolution inan embodiment is optional and not necessary for the other operations ofthe embodiment.

Fuzzy c-means (FCM) algorithm is a known algorithm that is used forclustering pixels into a foreground layer and a background layer. Anembodiment modifies the FCM algorithm to implement a new modified fuzzyc-means clustering technique. According to this modified technique, thelabeling of a pixel is influenced by the labels of the pixels that arein the immediate neighborhood of the pixel.

A label of a pixel is a designation whether the pixel belongs in theforeground layer or the background layer. The label of a pixel can beindicated using a Binary 0 or 1 value. As a non-limiting example, theBinary value of 0 is used as a label of a pixel to indicate that thepixel should be sent to the background layer. As a non-limiting example,the Binary value of 1 is used as a label of a pixel to indicate that thepixel should be sent to the foreground layer. A label of a pixel isrecorded, stored, or saved in the selection layer.

An immediate neighborhood of a pixel includes those pixels that areimmediately adjacent to the pixel, without any intervening pixelsbetween the pixel and a neighborhood pixel. For example, if a pixel hasa quadrilateral shape and if the pixels are arranged in rows andcolumns, a pixel would have eight neighbors—four neighboring pixels oneach of the four sides of the pixel, and four neighboring pixels at eachof the four vertices of the pixel.

Each neighborhood pixel also has a label. The labels of the neighborhoodpixels of a given pixel influence the label of the pixel. Generally,according to an embodiment, the effect of the neighborhood labels actsas a regularizer and biases the label of the given pixel. This manner ofassigning a label value to a pixel is called piecewise homogeneouslabeling (PHL). The modification to the fuzzy c-means algorithmcomprises PHL.

Furthermore, an embodiment applies the PHL modification in ablock-by-block manner. Specifically, the embodiment divides the originaldocument into a number of overlapping blocks of size n units by m units(n×m blocks), where n and m are any suitable number of units chosenaccording to an implementation. The units can be a measurement unit suchas inches or centimeters, or a count, such as pixels per inch or dotsper inch (DPI). The embodiment applies the modified FCM to an n×m blockand labels the pixels in that block as described herein.

The illustrative embodiments are described using single-bit Binary labelvalues and three layers only as non-limiting examples. An embodiment canbe adapted to use more than two label values to assign the pixels tomore than one foreground layer, more than one background layer, or acombination thereof. Such adaptations are contemplated within the scopeof the illustrative embodiments.

A method of an embodiment described herein, when implemented to executeon a device or data processing system, comprises substantial advancementof the functionality of that device or data processing system incompressing documents. For example, prior-art lossless compressionresults in insignificant compression in many common circumstances,prior-art lossy compression results in poor reproduction results upondecompression, and prior-art FCM is prone to misclassification of pixelsresulting in bloated compressed documents. The embodiments use theclassification of the neighborhood pixels to bias the classification ofa pixel. Operating in a manner described herein, an embodimentsignificantly reduces the size of the compressed document whilepreserving the information from the original document. Such manner ofcompressing documents is unavailable in presently available devices ordata processing systems. Thus, a substantial advancement of such devicesor data processing systems by executing a method of an embodimentimproves document compression and results in significant saving of datastorage space without loss of information.

The illustrative embodiments are described with respect to certaindocuments, pixels and pixel shapes, blocks, labels, planes or layers,neighborhoods, devices, data processing systems, environments,components, and applications only as examples. Any specificmanifestations of these and other similar artifacts are not intended tobe limiting to the invention. Any suitable manifestation of these andother similar artifacts can be selected within the scope of theillustrative embodiments.

Furthermore, the illustrative embodiments may be implemented withrespect to any type of data, data source, or access to a data sourceover a data network. Any type of data storage device may provide thedata to an embodiment of the invention, either locally at a dataprocessing system or over a data network, within the scope of theinvention. Where an embodiment is described using a mobile device, anytype of data storage device suitable for use with the mobile device mayprovide the data to such embodiment, either locally at the mobile deviceor over a data network, within the scope of the illustrativeembodiments.

The illustrative embodiments are described using specific code, designs,architectures, protocols, layouts, schematics, and tools only asexamples and are not limiting to the illustrative embodiments.Furthermore, the illustrative embodiments are described in someinstances using particular software, tools, and data processingenvironments only as an example for the clarity of the description. Theillustrative embodiments may be used in conjunction with othercomparable or similarly purposed structures, systems, applications, orarchitectures. For example, other comparable mobile devices, structures,systems, applications, or architectures therefor, may be used inconjunction with such embodiment of the invention within the scope ofthe invention. An illustrative embodiment may be implemented inhardware, software, or a combination thereof.

The examples in this disclosure are used only for the clarity of thedescription and are not limiting to the illustrative embodiments.Additional data, operations, actions, tasks, activities, andmanipulations will be conceivable from this disclosure and the same arecontemplated within the scope of the illustrative embodiments.

Any advantages listed herein are only examples and are not intended tobe limiting to the illustrative embodiments. Additional or differentadvantages may be realized by specific illustrative embodiments.Furthermore, a particular illustrative embodiment may have some, all, ornone of the advantages listed above.

With reference to the figures and in particular with reference to FIGS.1 and 2, these figures are example diagrams of data processingenvironments in which illustrative embodiments may be implemented. FIGS.1 and 2 are only examples and are not intended to assert or imply anylimitation with regard to the environments in which differentembodiments may be implemented. A particular implementation may makemany modifications to the depicted environments based on the followingdescription.

FIG. 1 depicts a block diagram of a network of data processing systemsin which illustrative embodiments may be implemented. Data processingenvironment 100 is a network of computers in which the illustrativeembodiments may be implemented. Data processing environment 100 includesnetwork 102. Network 102 is the medium used to provide communicationslinks between various devices and computers connected together withindata processing environment 100. Network 102 may include connections,such as wire, wireless communication links, or fiber optic cables.

Clients or servers are only example roles of certain data processingsystems connected to network 102 and are not intended to exclude otherconfigurations or roles for these data processing systems. Server 104and server 106 couple to network 102 along with storage unit 108.Software applications may execute on any computer in data processingenvironment 100. Clients 110, 112, and 114 are also coupled to network102. A data processing system, such as server 104 or 106, or client 110,112, or 114 may contain data and may have software applications orsoftware tools executing thereon.

Only as an example, and without implying any limitation to sucharchitecture, FIG. 1 depicts certain components that are usable in anexample implementation of an embodiment. For example, servers 104 and106, and clients 110, 112, 114, are depicted as servers and clients onlyas example and not to imply a limitation to a client-serverarchitecture. As another example, an embodiment can be distributedacross several data processing systems and a data network as shown,whereas another embodiment can be implemented on a single dataprocessing system within the scope of the illustrative embodiments. Dataprocessing systems 104, 106, 110, 112, and 114 also represent examplenodes in a cluster, partitions, and other configurations suitable forimplementing an embodiment.

Device 132 is an example of a device described herein. For example,device 132 can take the form of a smartphone, a tablet computer, alaptop computer, client 110 in a stationary or a portable form, awearable computing device, or any other suitable device. Any softwareapplication described as executing in another data processing system inFIG. 1 can be configured to execute in device 132 in a similar manner.Any data or information stored or produced in another data processingsystem in FIG. 1 can be configured to be stored or produced in device132 in a similar manner. Application 105 implements an embodimentdescribed herein.

Servers 104 and 106, storage unit 108, and clients 110, 112, and 114 maycouple to network 102 using wired connections, wireless communicationprotocols, or other suitable data connectivity. Clients 110, 112, and114 may be, for example, personal computers or network computers.

In the depicted example, server 104 may provide data, such as bootfiles, operating system images, and applications to clients 110, 112,and 114. Clients 110, 112, and 114 may be clients to server 104 in thisexample. Clients 110, 112, 114, or some combination thereof, may includetheir own data, boot files, operating system images, and applications.Data processing environment 100 may include additional servers, clients,and other devices that are not shown.

In the depicted example, data processing environment 100 may be theInternet. Network 102 may represent a collection of networks andgateways that use the Transmission Control Protocol/Internet Protocol(TCP/IP) and other protocols to communicate with one another. At theheart of the Internet is a backbone of data communication links betweenmajor nodes or host computers, including thousands of commercial,governmental, educational, and other computer systems that route dataand messages. Of course, data processing environment 100 also may beimplemented as a number of different types of networks, such as forexample, an intranet, a local area network (LAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

Among other uses, data processing environment 100 may be used forimplementing a client-server environment in which the illustrativeembodiments may be implemented. A client-server environment enablessoftware applications and data to be distributed across a network suchthat an application functions by using the interactivity between aclient data processing system and a server data processing system. Dataprocessing environment 100 may also employ a service orientedarchitecture where interoperable software components distributed acrossa network may be packaged together as coherent business applications.

With reference to FIG. 2, this figure depicts a block diagram of a dataprocessing system in which illustrative embodiments may be implemented.Data processing system 200 is an example of a computer, such as servers104 and 106, or clients 110, 112, and 114 in FIG. 1, or another type ofdevice in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments.

Data processing system 200 is also representative of a data processingsystem or a configuration therein, such as data processing system 132 inFIG. 1 in which computer usable program code or instructionsimplementing the processes of the illustrative embodiments may belocated. Data processing system 200 is described as a computer only asan example, without being limited thereto. Implementations in the formof other devices, such as device 132 in FIG. 1, may modify dataprocessing system 200, such as by adding a touch interface, and eveneliminate certain depicted components from data processing system 200without departing from the general description of the operations andfunctions of data processing system 200 described herein.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and memory controller hub (NB/MCH)202 and South Bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 arecoupled to North Bridge and memory controller hub (NB/MCH) 202.Processing unit 206 may contain one or more processors and may beimplemented using one or more heterogeneous processor systems.Processing unit 206 may be a multi-core processor. Graphics processor210 may be coupled to NB/MCH 202 through an accelerated graphics port(AGP) in certain implementations.

In the depicted example, local area network (LAN) adapter 212 is coupledto South Bridge and I/O controller hub (SB/ICH) 204. Audio adapter 216,keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224,universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234are coupled to South Bridge and I/O controller hub 204 through bus 238.Hard disk drive (HDD) or solid-state drive (SSD) 226 and CD-ROM 230 arecoupled to South Bridge and I/O controller hub 204 through bus 240.PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbinary input/output system (BIOS). Hard disk drive 226 and CD-ROM 230may use, for example, an integrated drive electronics (IDE), serialadvanced technology attachment (SATA) interface, or variants such asexternal-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device236 may be coupled to South Bridge and I/O controller hub (SB/ICH) 204through bus 238.

Memories, such as main memory 208, ROM 224, or flash memory (not shown),are some examples of computer usable storage devices. Hard disk drive orsolid state drive 226, CD-ROM 230, and other similarly usable devicesare some examples of computer usable storage devices including acomputer usable storage medium.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within dataprocessing system 200 in FIG. 2. The operating system may be acommercially available operating system such as AIX® (AIX is a trademarkof International Business Machines Corporation in the United States andother countries), Microsoft® Windows® (Microsoft and Windows aretrademarks of Microsoft Corporation in the United States and othercountries), Linux® (Linux is a trademark of Linus Torvalds in the UnitedStates and other countries), iOS™ (iOS is a trademark of Cisco Systems,Inc. licensed to Apple Inc. in the United States and in othercountries), or Android™ (Android is a trademark of Google Inc., in theUnited States and in other countries). An object oriented programmingsystem, such as the Java™ programming system, may run in conjunctionwith the operating system and provide calls to the operating system fromJava™ programs or applications executing on data processing system 200(Java and all Java-based trademarks and logos are trademarks orregistered trademarks of Oracle Corporation and/or its affiliates).

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs, such as application 105 in FIG. 1,are located on storage devices, such as hard disk drive 226, and may beloaded into at least one of one or more memories, such as main memory208, for execution by processing unit 206. The processes of theillustrative embodiments may be performed by processing unit 206 usingcomputer implemented instructions, which may be located in a memory,such as, for example, main memory 208, read only memory 224, or in oneor more peripheral devices.

The hardware in FIGS. 1-2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS.1-2. In addition, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system.

In some illustrative examples, data processing system 200 may be apersonal digital assistant (PDA), which is generally configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data. A bus system may comprise one or morebuses, such as a system bus, an I/O bus, and a PCI bus. Of course, thebus system may be implemented using any type of communications fabric orarchitecture that provides for a transfer of data between differentcomponents or devices attached to the fabric or architecture.

A communications unit may include one or more devices used to transmitand receive data, such as a modem or a network adapter. A memory may be,for example, main memory 208 or a cache, such as the cache found inNorth Bridge and memory controller hub 202. A processing unit mayinclude one or more processors or CPUs.

The depicted examples in FIGS. 1-2 and above-described examples are notmeant to imply architectural limitations. For example, data processingsystem 200 also may be a tablet computer, laptop computer, or telephonedevice in addition to taking the form of a mobile or wearable device.

With reference to FIG. 3, this figure depicts a block diagram of anexample document compression method in accordance with an illustrativeembodiment. Document 302 is an example document with text and image datathat is to be compressed. Document 302 is depicted in gray scale onlydue to limitations of patent application drawings. Document 302 can, andoften does include colors as a part of the image portions of thedocument, text portions of the document, or a combination thereof.

A modified FCM process according to an embodiment breaks down document302 into mask layer 304, foreground layer 306, and background layer 308.A decompression, reconstruction, or reproduction process uses mask layer304 to reconstruct original document 302 from foreground layer 306 andbackground layer 308.

Foreground layer 306 contains text, edges, and line art colors. Anembodiment prevents artifacts such as dots of a certain size or largerand noise information, e.g. imperfections due to dirt, dust, printingaberrations commonly seen as salt-and-pepper pixels scattered in adocument, from appearing in foreground layer 306 as a part of the textor edges. Low resolution is usually sufficient to represent the regioncolors of these artifacts.

Background layer 308 contains images and graphics. Low resolution issometimes sufficient to represent artifacts in this layer as long as thecompression is set to high quality or minimal loss.

With reference to FIG. 4, this figure depicts a block diagram of anexample process for document compression with neighborhood biased pixellabeling in accordance with an illustrative embodiment. Document 402 isan example of document 302 in FIG. 3, and has similar characteristics.For example, document 402 can include any number or types of graphics orimages 402A located anywhere in the document, any number or types oftext 402B located anywhere in the document, or some combination thereof.

Application 404 is an example of application 105 in FIG. 1. Application404 receives document 402 for compression as received document 403.Application 404 divides received document 403 into several n×m blocks,such as block 406. “n” and “m” values can be set as suitable in a givenimplementation for a given received document 403. While the n×m blockssuch as block 406 are depicted as adjacent to one another, the blocksmay be overlapping such that a portion of received document 403 may beincluded in two or more blocks.

Example contents of block 406 are shown in enlarged view 406A of block406. View 406A shows that block 406 contains all or portions of lettersof text 402B, all or portions of some images 402A, or some combinationthereof. These contents have edges that are to be detected according tothe modified FCM of an embodiment. For example, the thickness of a lineof a letter includes an edge where pixel color or intensity changes ortransitions by an amount that is greater than a threshold.

The threshold can be set to detect coarse transitions, for example, atransition of at least 0FFFFF hex, which would detect an edge in atransition from black (color value 000000 hex) pixel color to white(color value FFFFFF hex) pixel color, and few other edges in receiveddocument 403. The threshold can be set to detect fine transitions, forexample, a transition of at least 00000A hex, which would detect an edgein a transition from pixel color value 0000AA hex to pixel color value0000BA hex, and many other edges in received document 403.

Any degree of transition can thus be detected by appropriately setting athreshold, such as to detect “strong” edges, “medium” edges, or “light”edges. When multiple transitions are to be detected, such as to detectedges between different transitions, multiple thresholds can be set in asimilar manner.

A block, such as block 406, which includes a threshold number of edges,is called an edge block. The threshold number of edges that must bepresent in a block for the block to be an edge block is configurableaccording to the implementation, according to received document 403, ora combination thereof. A very low threshold, e.g., 1 (an arbitraryexample), allows any block with the low threshold number of edgetransitions to qualify as an edge block. As will be apparent from thisdisclosure to those of ordinary skill in the art, a large number of edgeblocks increases the computation costs and may also increase the size ofa resulting compressed document due to a heavier foreground layer. Avery high threshold, e.g., 1000 (an arbitrary example), allows onlythose block with the high threshold number of edge transitions toqualify as an edge block. As will be apparent from this disclosure tothose of ordinary skill in the art, a very small number of edge blocksresults in a poor quality reproduction of document 402 upondecompression.

For a block that does not qualify as an edge block, application 404labels all pixels in such a block to be sent to the background layer.For a block that does qualify as an edge block, component 408, whichimplements the PHL mechanism according to an embodiment and performs themodified FCM processing of pixels of the edge block, labels the pixelsof the edge block. Particularly, component 408 labels certain pixels(410) of example edge block 406 to be sent to foreground layer 416 inlocation 406B corresponding to the location of block 406 in document403. Similarly, component 408 labels certain pixels (412) of exampleedge block 406 to be sent to background layer 418 in location 406Ccorresponding to the location of block 406 in document 403. Application404 stores these labels corresponding to pixels 410 and 412 in masklayer 414.

When all the blocks are processed in this manner, mask layer 414,foreground layer 416, and background layer 418 are complete for receiveddocument 403. Application 404 outputs mask layer 414, foreground layer416, and background layer 418 to combining component 420. Combiningcomponent 420 combines mask layer 414, foreground layer 416, andbackground layer 418, and outputs compressed document 422 correspondingto document 402. In one embodiment, compressed document 422 is adocument in Portable Document Format (PDF).

With reference to FIG. 5, this figure depicts a block diagram of anexample pixel neighborhood for PHL in accordance with an illustrativeembodiment. Edge block 502 is an example of edge block 406 in FIG. 4.

Only as an example and without implying any limitation thereto, assumethe following—edge block 502 includes all or a part of letter 504, andedge 506 is an edge where pixels transition from the color of letter 504to the background color.

Pixels that satisfy the transition thresholds in edge block 502 areinitially labeled according to the thresholds. Thus, at least some ofthe pixels in edge block 502 will be labeled before proceeding to thenext step. As to the next step, consider pixel labeled “0” in pixelneighborhood 508. Pixel 0 has, as neighbors, pixels 1, 2, 3, 4, 5, 6, 7,and 8 in neighborhood 508. Each of pixels 1-8 in neighborhood 508 arewithin the color of letter 504. Assume that some or all of pixels 1-8 inneighborhood 508 have been labeled as belonging to letter 504 andtherefore belonging on foreground layer 416 in FIG. 4. Pixel 0 has notbeen labeled yet or has been labeled such that pixel 0 will cause saltand pepper type artifacts. Assume, for example, that pixels onforeground layer are labeled 1. Therefore, pixels 1-8 in neighborhood508 are labeled 1 in this example.

Further assume that pixel 0 is of the background color due to anaberration in document 402. A prior-art method would put pixel 0 ofneighborhood 508 on background layer 418. However, according to anembodiment, at the time of labeling pixel 0, PHL component 408 takesinto account the labels that exist in neighborhood 508. Even if pixel 0were of the background color due to an aberration in document 402, PHLcomponent would label pixel 0 as “1” for placing pixel 0 of neighborhood508 on foreground layer 416.

A similar operation would place pixels 0-8 of neighborhood 510 onbackground layer 418. Thus, individual pixel positions and colors,although being a factor, are not the only determiner in the placement ofthat pixel during decomposition for compression. The labeling of a pixelis biased, or influenced, by the labels that exist in the immediateneighborhood of the pixel in question.

The neighborhood pixels can be a mix of different labels, and anappropriate strategy is used to weigh them into the determination of alabel for a pixel. One simplified example would determine whether thereare more pixels in the neighborhood with one label as compared to thenumber of pixels in the neighborhood with the other label. Accordingly,the label of the pixel in question is biased towards the label that isin majority in the neighborhood. The final label of the pixel inquestion is only biased, not conclusively determined by theneighborhood. Other considerations for the labeling can also contributeto the determination of the final label for the pixel with the biasimparted by the neighborhood pixels.

Another generalized non-limiting manner of implementing the modified FCMwith the PHL is as follows—

The standard FCM objective function for partitioning {x_(k)}_(k=1) ^(N)into C clusters is given by

$\begin{matrix}{J = {\sum\limits_{i = 1}^{c}{\sum\limits_{k = 1}^{N}{\mu_{ik}^{p}{{x_{k} - v_{i}}}^{2}}}}} & (1)\end{matrix}$

Where {v_(i)}_(i=1) ^(c) are the prototypes of the clusters and thearray [u_(ik)]=U represents a partition matrix, U∈U, namely

$\begin{matrix}{U\left\{ {{{u_{ik} \in \left\lbrack {0,1} \right\rbrack}❘{\sum\limits_{i = 1}^{c}u_{ik}}} = {1{\forall{{k\mspace{14mu}{and}\mspace{14mu} 0} < {\sum\limits_{k = 1}^{N}u_{ik}} < {N{\forall i}}}}}} \right\}} & (2)\end{matrix}$

The parameter p is a weighting exponent on each fuzzy membership anddetermines the amount of fuzziness of the resulting classification. TheFCM objective function is minimized when high membership values areassigned to pixels whose intensities are close to the centroid of itsparticular class, and low membership values are assigned when the pixeldata is far from the centroid.

A modified FCM algorithm according to an embodiment is formulated bymodifying the objective function of the standard FCM algorithm. Thismodified formulation allows the labeling of a pixel to be influenced bythe labels in its immediate neighborhood.

According to an embodiment, a modification to equation (1), above,introduces a term that allows the labeling of a pixel to be influencedby the labels in its immediate neighborhood. The neighborhood effectacts as a regularizer and biases the solution towardspiecewise-homogeneous labeling. Such regularization is useful insegmenting documents that have been corrupted by a variety of noisesources, such as by introduction of salt and pepper noise into a scan ofa document. The modified objective function is given by

$\begin{matrix}{J_{m} = {{\sum\limits_{i = 1}^{c}{\sum\limits_{k = 1}^{N}{\mu_{ik}^{p}{{x_{k} - v_{i}}}^{2}}}} + {\frac{\alpha}{N_{R}}{\sum\limits_{i = 1}^{c}{\sum\limits_{k = 1}^{N}{\mu_{ik}^{p}\left( {\sum\limits_{x_{r} \in N_{k}}{{x_{r} - v_{i}}}^{2}} \right)}}}}}} & (3)\end{matrix}$

Where N_(k) stands for the set of neighbors that exist in a windowaround x_(k) and N_(R) is the cardinality of N_(k). The effect of theneighbors term is controlled by the parameter α. The relative importanceof the regularizing term is inversely proportional to the signal tonoise ratio (SNR) of the image signal. Lower SNR would require a highervalue of the parameter α.

Formally, the optimization problem comes in the form

$\begin{matrix}{{\min\limits_{U,{\{ v_{i}\}}_{i = 1}^{c},{\{\beta_{k}\}}_{k = 1}^{N}}{J_{m}\mspace{14mu}{subject}\mspace{14mu}{to}\mspace{14mu} U}} \in U} & (4)\end{matrix}$

The new penalty term is minimized when the membership value for aparticular class is large and the membership values for the otherclasses at neighboring pixels are small (and vice versa). In otherwords, it constrains the membership value of a class to be negativelycorrelated with the membership values of the other classes atneighboring pixels.

Membership Evaluation

The zero-gradient condition for the membership estimator can berewritten as,

$\begin{matrix}{{{u_{ik}^{*} = {\frac{1}{{\Sigma_{j = 1}^{c}\left( \frac{D_{ik} + {\frac{\alpha}{N_{R}}\gamma_{i}}}{D_{jk} + {\frac{\alpha}{N_{R}}\gamma_{j}}} \right)}^{\frac{1}{p - 1}}}\mspace{14mu}{where}}}D_{ik} = {{x_{k} - v_{i}}}^{2}}{\gamma_{i} = {\left( {\sum\limits_{x_{r} \in N_{k}}{{x_{r} - v_{i}}}^{2}} \right).}}} & (5)\end{matrix}$

Cluster Prototype Updating

$\begin{matrix}{v_{i}^{*} = \frac{\sum\limits_{k = 1}^{N}{\mu_{ik}^{p}\left( {x_{k} + {\frac{\alpha}{N_{R}}{\sum\limits_{x_{r} \in N_{k}}x_{r}}}} \right)}}{\left( {1 + \alpha} \right){\sum\limits_{k = 1}^{N}\mu_{ik}^{p}}}} & (6)\end{matrix}$

The other generalized non-limiting manner of implementing the modifiedFCM with the PHL ends here.

With reference to FIG. 6, this figure depicts a block diagram of acompression of layers resulting from a modified FCM process inaccordance with an illustrative embodiment. Mask layer 602 is an exampleof mask layer 414, foreground layer 604 is an example of foregroundlayer 416, and background layer 606 is an example of background layer418 in FIG. 4.

Application 404 of FIG. 4 compresses each of layers 602, 604, and 606according to the type of data stored in that layer. For example, masklayer 602 has binary data corresponding to the labels of the pixels ofthe original document as described elsewhere in this disclosure. Masklayer 602 should therefore be compressed using a Binary compression,preferably in a lossless manner. G4 and JBIG2 are some examples of suchcompression techniques that can be used to compress mask layer 602.

Because of the type of pixel information stored therein, foregroundlayer 604 can be compressed, for example, using a low resolution lowquality compression, e.g., JPEG compression. Similarly, because of thetype of pixel information stored therein, background layer 606 can becompressed, for example, using a low resolution high qualitycompression, e.g., JPEG compression.

Again, the quality and resolution of the various compressions depictedand described with respect to FIG. 6 are only examples and not intendedto be limiting on the illustrative embodiments. These examples have beenselected only to show that different compressions are usable withdifferent layers according to the data stored in those layers. Dependingupon a particular implementation, a particular document, a particularcompression requirement, or a combination thereof, other compressiontechniques are similarly usable with layers 602, 604, and 606, and othersimilar layers, if any, within the scope of the illustrativeembodiments.

With reference to FIG. 7, this figure depicts a flowchart of an exampleprocess for document compression with neighborhood biased pixel labelingin accordance with an illustrative embodiment. Process 700 can beimplemented in application 404 in FIG. 4.

The application receives a document for compression (step 702). Theapplication divides the document into n×m blocks that may be touching oroverlapping (step 704).

The application selects an n×m block (step 706). The applicationdetermines whether the selected block includes at least a thresholdnumber of edges (step 708). If the selected block does not include atleast the threshold number of edges (“No” path of step 708), theapplication classifies the block as a non-edge block (step 710). Theapplication marks, in the mask layer, all pixels of that selected blockas background pixels (step 712). The application proceeds to block 730thereafter.

If the selected block includes at least the threshold number of edges(“Yes” path of step 708), the application classifies the block as anedge block (step 714). The application then decides which pixels of theedge block will go to the foreground layer and which pixels of the edgeblock will go to the background layer (step 716).

Particularly, the application in executing block 716, selects a pixelfrom the edge block (step 718). The application determines a class of aneighboring pixel (step 720). A class of a pixel is a label of a pixel.

Accordingly, a pixel whose label indicates that the pixel belongs in aforeground layer has a foreground class, and a pixel whose labelindicates that the pixel belongs in a background layer has a backgroundclass.

The application classifies the selected pixel using a modified FCMalgorithm described herein, using the classes of the neighboring pixelsas bias on the classification (step 722). Note that not all neighboringpixels need to be considered for such influence. In other words, anynumber of immediate neighbor pixels can be used in step 722.

The application labels the selected pixel as a background pixel or aforeground pixel according to the classification bias from theneighboring pixels (step 724). The application repeats steps 718-724 forall pixels in the edge block.

The application determines whether more blocks created in step 704remain to be processed in this manner (step 730). If more blocks remain(“Yes” path of step 730), the application returns process 700 to step706 to select another block.

If no more blocks remain (“No” path of step 730), the applicationproduces the completed mask layer, the foreground layer, and thebackground layer (step 732). The application optionally combines thelayers into a compressed document, e.g., a PDF document (step 734). Theapplication optionally outputs the compressed document, e.g., the PDFdocument as the compressed document (step 736). The application endsprocess 700 thereafter.

Thus, a computer implemented method, system or apparatus, and computerprogram product are provided in the illustrative embodiments fordocument compression with neighborhood biased pixel labeling. Where anembodiment or a portion thereof is described with respect to a type ofdevice, the computer implemented method, system or apparatus, thecomputer program product, or a portion thereof, are adapted orconfigured for use with a suitable and comparable manifestation of thattype of device.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

What is claimed is:
 1. A computer system for compressing a document, thecomputer system comprising one or more processors, one or morecomputer-readable memories, and one or more computer-readable storagedevices, and program instructions stored on at least one of the one ormore storage devices for execution by at least one of the one or moreprocessors via at least one of the one or more memories, the storedprogram instructions comprising: program instructions to count a numberof edges present in a selected portion of the document; programinstructions to determine whether the number of edges exceeds athreshold number of edges; program instructions to select, responsive tothe number of edges exceeding the threshold number of edges, a pixelfrom the portion; program instructions to identify, for the pixel, a setof neighboring pixels; program instructions to identify, for eachneighboring pixel in a subset of the set of neighboring pixels, acorresponding label of the neighboring pixel, wherein a mask layercorresponding to the document contains labels of pixels in the portion;program instructions to bias, in the mask layer, a label of the selectedpixel using labels of neighboring pixels in the subset of theneighboring pixels; program instructions to designate, according to thelabel of the selected pixel, the selected pixel to one of a foregroundlayer corresponding to the document and a background layer correspondingto the document; and program instructions to construct, corresponding tothe document, a compressed document using the mask layer, the foregroundlayer, and the background layer.
 2. The computer system of claim 1,wherein no intervening pixel is present between a neighboring pixel inthe set of neighboring pixels and the pixel in the portion.
 3. Thecomputer system of claim 1, further comprising: program instructions toreceive the document; and program instructions to divide the documentinto a set of blocks, each block in the set being of a specified size,wherein the portion is a block in the set of blocks.
 4. The computersystem of claim 3, further comprising: program instructions to form theblocks in the set of blocks such that at least one block in the set ofblocks overlaps at least one other block in the set of blocks.
 5. Thecomputer system of claim 1, further comprising: program instructions todetermine whether a second portion of the document includes thethreshold number of edges; and program instructions to label, responsiveto the second portion not including the threshold number of edges, allpixels in the second portion as belonging in the background layer. 6.The computer system of claim 1, further comprising: program instructionsto detect an edge in the portion by determining whether a transitionfrom a color of a first pixel in the portion to a color of a secondpixel is greater than a threshold amount of transition.
 7. The computersystem of claim 6, further comprising: program instructions to define aset of threshold amounts of transitions, wherein the threshold amount oftransition is a member of the set of threshold amounts of transitions;and program instructions to use a different threshold amount oftransition from the set of threshold amounts of transitions to detect adifferent type of edge in the portion.
 8. The computer system of claim1, wherein the subset of the set of neighboring pixels is the entire setof neighboring pixels.
 9. The computer system of claim 1, wherein aplurality of foreground layers correspond to the document, eachforeground layer in the plurality of foreground layer corresponding to adifferent label value of pixels in the document.
 10. The computer systemof claim 1, wherein a plurality of background layers correspond to thedocument, each background layer in the plurality of background layercorresponding to a different label value of pixels in the document. 11.The computer system of claim 1, further comprising: program instructionsto complete constructing the mask layer, the foreground layer, and thebackground layer; and program instructions to combine, as a part ofconstructing, the completed mask layer, the completed foreground layer,and the completed background layer to form the compressed document. 12.The computer system of claim 11, wherein the program instructions tocombine uses a Portable Document Format (PDF), and the compresseddocument is a PDF document.
 13. The computer system of claim 1, whereinthe program instructions to identify, the program instructions to bias,and the program instructions to designate comprise a PiecewiseHomogeneous Labeling (PHL) method, and the PHL method modifies a FuzzyC-Means method of pixel classification.